Women into Non-Traditional Sectors: Addressing Gender Segregation in the Northern Ireland Workforce

The horizontal segregation of the workforce along gender lines tends to assign women to lower paid, lower status employment. Consequently, schemes to address segregation have focused on preparing women to enter non‐traditional occupations through training and development processes. This article examines models to encourage women into non‐traditional employment, focusing on the Women into Non‐Traditional Sectors (WINS) project in Belfast, Northern Ireland. However, changing women to suit a hostile work environment assumes women to be the problem, whereas it is the barriers that women face in undertaking non‐traditional jobs that need to be changed. It is concluded, therefore, that while models such as WINS can be successful in assisting women into non‐traditional sectors, change processes to make workplaces more accessible are a more pressing and appropriate approach to de‐segregating the workforce.

Multi-scale colorectal tumour segmentation using a novel coarse to fine strategy

This paper addresses the problem of colorectal tumour segmentation in complex real world imagery. For efficient segmentation, a multi-scale strategy is developed for extracting the potentially cancerous region of interest (ROI) based on colour histograms while searching for the best texture resolution. To achieve better segmentation accuracy, we apply a novel bag-of-visual-words method based on rotation invariant raw statistical features and random projection based l2-norm sparse representation to classify tumour areas in histopathology images. Experimental results on 20 real world digital slides demonstrate that the proposed algorithm results in better recognition accuracy than several state of the art segmentation techniques.